Cloud Types Research Articles

A prediction method for aircraft icing severity forecast based on probability model and meteorological observation data

Aircraft flight performance and safety are inevitably affected by adverse meteorological conditions, one such weather is icing. Aircraft icing can cause severe aerodynamic and flight mechanical effects, thus threatens aircraft flight safety. This study presents an aircraft icing severity prediction method with probabilistic model, in which meteorological observation data are described as random variables and the airplane icing severity level is defined as fuzzy variables with membership function. When the information of aircraft flying cloud type, altitude, speed, temperature, pressure and dew point temperature is known, the probability of aircraft icing severity during aircraft fly can be effectively predicted by a weighted icing forecasting method. Using the proposed method into two aircraft icing cases, the analysis results show that the proposed method is accurate and valid. It can provide technical support for aircraft icing forecasting.

Deterministic Global 3D Fractal Cloud Model for Synthetic Scene Generation

This paper describes the creation of a fast, deterministic, 3D fractal cloud renderer for the AFIT Sensor and Scene Emulation Tool (ASSET). The renderer generates 3D clouds by ray marching through a volume and sampling the level-set of a fractal function. The fractal function is distorted by a displacement map, which is generated using horizontal wind data from a Global Forecast System (GFS) weather file. The vertical windspeed and relative humidity are used to mask the creation of clouds to match realistic large-scale weather patterns over the Earth. Small-scale detail is provided by the fractal functions which are tuned to match natural cloud shapes. This model is intended to run quickly, and it can run in about 700 ms per cloud type. This model generates clouds that appear to match large-scale satellite imagery, and it reproduces natural small-scale shapes. This should enable future versions of ASSET to generate scenarios where the same scene is consistently viewed from both GEO and LEO satellites from multiple perspectives.

Comparative Study of Single-Wood Biomass Model at Plot Level Based on Multi-Source LiDAR

Forests play an important role in promoting carbon cycling and mitigating the urban heat island effect as one of the world’s major carbon storages. Scientifically quantifying tree biomass is the basis for assessing tree carbon storage and other ecosystem functions. In this study, a sample plot of Populus tomentosa plantation in the Olympic Forest Park in Beijing was selected as the research object. Point cloud data from three types of laser scanners, including terrestrial laser scanner (TLS), backpack laser scanner (BLS), and handheld laser scanner (HLS), were used to estimate the biomass of single tree trunks, branches, leaves, and aboveground total biomass based on the Allometric Biomass Model (ABM) and Advanced Quantitative Structure Model (AdQSM). The following conclusions were drawn from the estimation results: (1) For the three types of laser scanner point clouds, the biomass estimation values obtained using the AdQSM model were generally higher than those obtained using the Allometric Biomass Model. However, the estimation values obtained using the two models were similar, especially for tree trunks and total biomass. (2) For total biomass and individual biomass components of single trees, the results obtained from handheld and terrestrial laser scanner point clouds are consistent; however, they show some differences from the results obtained from backpack-mounted point clouds. This study further enriches the methodological system for estimating forest biomass, providing a theoretical basis and reference for more accurate estimates of forest biomass and more sustainable forest management.

Estimation of monthly sunshine duration using satellite derived cloud data

Sunshine duration (SD) is one of the critical meteorological parameters used in different fields of application such as climate, renewable energy and agriculture. In this respect, determination and/or estimation of the temporal and spatial variability of SD is critical. Meteorological satellite data/products can be used for estimating SD and in constructing their maps due to their frequent observation of large areas at once. In this study, a multilayer perceptron type artificial neural network model was built to estimate the monthly mean SD for Türkiye using the EUMETSAT CM SAF (Satellite Application Facility on Climate Monitoring) CFC (Cloud Fractional Coverage) and CTY (Cloud Type) data, GMTED2010 (Global Multi-resolution Terrain Elevation Data) data, month number and daylength. The datasets of 45 stations, spanning nine years (2005–2013), were used for training the model and 12 stations for testing and validating the simulated values. We have compared the results of our model with the ground-measured values for the whole period under consideration and the root mean square error (RMSE), mean absolute error (MAE), mean bias error (MBE) and the coefficient of determination (R2) were found as 0.7803 h, 0.6206 h, 0.1751 h and 0.9387, respectively. It has been shown that using the new generation cloud products such as CFC and CTY, elevation data such as GMTED2010 and daylength, it is possible to predict the SD for regions under the coverage of the satellite, in case no measurement is possible or may be unreliable, without needing any measured meteorological data.

Solar Irradiance Forecasting with Natural Language Processing of Cloud Observations and Interpretation of Results with Modified Shapley Additive Explanations

Forecasting the generation of solar power plants (SPPs) requires taking into account meteorological parameters that influence the difference between the solar irradiance at the top of the atmosphere calculated with high accuracy and the solar irradiance at the tilted plane of the solar panel on the Earth’s surface. One of the key factors is cloudiness, which can be presented not only as a percentage of the sky area covered by clouds but also many additional parameters, such as the type of clouds, the distribution of clouds across atmospheric layers, and their height. The use of machine learning algorithms to forecast the generation of solar power plants requires retrospective data over a long period and formalising the features; however, retrospective data with detailed information about cloudiness are normally recorded in the natural language format. This paper proposes an algorithm for processing such records to convert them into a binary feature vector. Experiments conducted on data from a real solar power plant showed that this algorithm increases the accuracy of short-term solar irradiance forecasts by 5–15%, depending on the quality metric used. At the same time, adding features makes the model less transparent to the user, which is a significant drawback from the point of view of explainable artificial intelligence. Therefore, the paper uses an additive explanation algorithm based on the Shapley vector to interpret the model’s output. It is shown that this approach allows the machine learning model to explain why it generates a particular forecast, which will provide a greater level of trust in intelligent information systems in the power industry.

Plant-Denoising-Net (PDN): A plant point cloud denoising network based on density gradient field learning

Effective point cloud denoising is critical in 3D plant phenotyping applications, which reduces interference in subsequent algorithms and improves the accuracy of plant phenotypes measurement. Deep learning-based point cloud denoising algorithms have shown excellent denoising performance on simple CAD models. However, these algorithms suffer from issues including over-smoothing or shrinkage and low efficiency when applied on density uneven, incomplete, various types of noise and complex plant point clouds. We proposed a plant point cloud denoising network (PDN) based on point cloud density gradient field learning, which can effectively address the challenges posed by plant point clouds. PDN consists of three main modules: point density feature (PDF) exception module, umbrella operator feature (UOF) computation module, and point density gradient (DG) estimation module. The performance of PDN was evaluated in experiments using point clouds of multiple plant species with noise of different types. Under different levels of Gaussian noise, our method achieved a relative performance improvement of 7.6%-19.3% compared to the state-of-the-art baseline methods, reaching state-of-the-art denoising performance. For noise of different types, the majority of our denoising results outperformed the baseline methods. In addition, our method was 0.5 and 8.6 times faster than the baseline methods when processing point clouds with low and high noise level, respectively. The good robustness, generalization, and computational efficacy of PDN are expected to facilitate the acquisition of high-precision 3D point clouds for various plant species, enhance the versatility of 3D phenotyping methods, improve the accuracy of the measurement of structural phenotypes, and increase the throughput of data processing, therefore facilitate the development of modern breeding research. The source code and the datasets used in this study is available on GitHub at https://github.com/suetme/PDN-plant-denoising-net.

Absorption and Self-absorption of [C ii] and [O i] Far Infrared Lines toward a Bright Bubble in the Nessie Infrared Dark Cloud

Using the upGREAT instrument on board the Stratospheric Observatory for Infrared Astronomy, we imaged [C ii] 157.74 and [O i] 63.18 μm line emission from a bright photodissociation region (PDR) associated with an ionized bubble located in the Nessie Nebula, a filamentary infrared dark cloud. A comparison with Australia Telescope Compact Array data reveals a classic photodissociation region (PDR) structure, with a uniform progression from ionized gas, to photodissociated gas, and to molecular gas from the bubble’s interior to its exterior. [O i] line emission from the bubble’s PDR reveals self-absorption features. Toward a far-IR bright protostar, both [O i] and [C ii] show an absorption feature at a velocity of −18 km s−1, the same velocity as an unrelated foreground molecular cloud. Since the gas density in typical molecular clouds is well below the [O i] and [C ii] critical densities, the excitation temperatures for both lines are low (∼20 K). The Meudon models demonstrate that the surface of a molecular cloud, externally illuminated by a standard G 0 = 1 interstellar radiation field, can produce absorption features in both transitions. Thus, the commonly observed [O i] and [C ii] self-absorption and absorption features plausibly arise from the subthermally excited, externally illuminated photodissociated envelopes of molecular clouds. The luminous young stellar object AGAL337.916-00.477, located precisely where the expanding bubble strikes the Nessie filament, is associated with two shock tracers: NH3 (3,3) maser emission and SiO 2−1 emission, indicating an interaction between the bubble and the filament. The interaction of the expanding bubble with its parental dense filament has triggered star formation.

ОБРАБОТКА СПУТНИКОВОЙ ИНФОРМАЦИИ В ИНТЕРЕСАХ ГИДРОМЕТЕОРОЛОГИИ С ИСПОЛЬЗОВАНИЕМ НЕЙРОСЕТЕВЫХ ТЕХНОЛОГИЙ: ПОДХОДЫ НИЦ “ПЛАНЕТА”

The paper presents an experience of using artificial intelligence techniques, in particular, neural networks to solve relevant problems of hydrometeorology. The results of the investigations at the Planeta State Research Center on Space Hydrometeorology in detecting clouds and snow cover from the Himawari, Electro-L, and Meteor-M satellite data, as well as on classifying cloud types according to the AHI instrument data (Himawari-8) are reported. The findings of the work on retrieving values of total ozone and water vapor according to the infrared sensing devices are demonstrated. The work on detecting the boundaries of the ice cover and river floods from medium- and high-resolution satellite instruments, as well as the technologies for temperature and humidity sensing in the microwave spectrum are considered. The studies have shown that the use of neural network technologies provides the required accuracy of the received hydrometeorological information and high speed of processing incoming data.

Physical characteristics of convective and non-convective cirrus clouds from CALIPSO data over the South China Sea

Studying the characteristics and mechanisms of convective and non-convective cirrus clouds over the South China Sea is vital for their impact on regional climate dynamics, and enhancing predictive models for weather and climate forecasts. This study utilizes eight years of CALIPSO data (from March 2007 to February 2015) to investigate convective and non-convective cirrus clouds. Explicit new insights include the observation that convective cirrus cloud samples are three times more numerous than non-convective cirrus clouds. Convective cirrus clouds are associated with humid conditions and demonstrate higher ice water content (IWC) values ranging from 10−3 to 10−1 g m−3, whereas non-convective cirrus clouds tend to be drier, exhibiting IWC values ranging from 10−4 to 10−3 g m−3. Both cirrus cloud types exhibit a maximum cloud fraction at 10°N. Convective cirrus reach their peak cloud fraction at an altitude of 14 km, while non-convective cirrus typically occur at altitudes between 15 and 16 km. The seasonal variability of the convective cirrus cloud fraction primarily reflects bottom-up positive specific humidity anomalies originating from convective activity, whereas the non-convective cirrus cloud fraction is influenced by top-down negative temperature anomalies.

Why does a decrease in cloud amount increase terrestrial evapotranspiration in a monsoon transition zone?

Terrestrial evapotranspiration plays a critical role in drought monitoring and water resource management. Changes in evapotranspiration are significantly influenced by cloud-related precipitation and radiation effects. However, the impact of cloud amount (CA) on evapotranspiration through its influence on precipitation remains uncertain, especially in the transition zone affected by the East Asian summer monsoon (EASM), which limits the understanding of the water cycle. Therefore, this study deeply explores the impact of CA on evapotranspiration and its potential physical mechanisms in Northwest China. The results show that the correlation between 31-year average evapotranspiration and CA is negative only in the semi-arid region and is positive in other climatic regions of Northwest China. This unique negative correlation is related to the change of precipitation pattern in the semi-arid region caused by the weak EASM. Smaller CA in weak monsoons results in more short-wave radiation reaching the surface, larger sensible heat, and weaker convective inhibition. Consequently, the proportion of convective clouds (CCs) increases and precipitation from these CCs enhances evapotranspiration. Less CA increases evapotranspiration and potentially exacerbates aridity in the semi-arid region of Northwest China. These results emphasize the role of cloud type in evapotranspiration. It is well known that global warming can change cloud type with more CCs. Therefore, this study sheds new light on evapotranspiration change under global warming.

Emulation of Cloud Microphysics in a Climate Model

AbstractWe present a machine learning based emulator of a microphysics scheme for condensation and precipitation processes (Zhao‐Carr) used operationally in a global atmospheric forecast model (FV3GFS). Our tailored emulator architecture achieves high skill (≥94%) in predicting condensate and precipitation amounts and maintains low global‐average bias (≤4%) for 1 year of continuous simulation when replacing the Fortran scheme. The stability and success of this emulator stems from key design decisions. By separating the emulation of condensation and precipitation processes, we can better enforce physical priors such as mass conservation and locality of condensation, and the vertical dependence of precipitation falling downward, using specific network architectures. An activity classifier for condensation imitates the discrete‐continuous nature of the Fortran microphysics outputs (i.e., tendencies are identically zero where the scheme is inactive, and condensate is zero where clouds are fully evaporated). A temperature‐scaled conditional loss function ensures accurate condensate adjustments for a high dynamic range of cloud types (e.g., cold, low‐condensate cirrus clouds or warm, condensate‐rich clouds). Despite excellent overall performance, the emulator exhibits some deficiencies in the uppermost model levels, leading to biases in the stratosphere. The emulator also has short episodic skill dropouts in isolated grid columns and is computationally slower than the original Fortran scheme. Nonetheless, our challenges and strategies should be applicable to the emulation of other microphysical schemes. More broadly, our work demonstrates that with suitable physically motivated architectural choices, ML techniques can accurately emulate complex human‐designed parameterizations of fast physical processes central to weather and climate models.

Evaluating the cloud effect on solar irradiation by three-dimensional cloud information

A detailed analysis of the effect of clouds on irradiance yields insights into the interaction between clouds and radiation and the precise forecasting of short-term solar radiation. Existing research has mainly focused on the impact of the cloud fraction (CF), i.e., the area distribution of clouds, on radiation. However, three-dimensional cloud information, such as the cloud thickness, also significantly affects solar radiation. This study proposes an innovative index based on the luminance of clouds in all-sky images (ASIs) to characterize the three-dimensional information of clouds, namely, CT, which is usually omitted in the binarization of ASIs. By using two-year synchronous ground irradiance and ASI observation data, the relationship between CT and the cloud radiative effect (CRE) is studied for different types of clouds under two situations, the sun being obscured and unobscured by clouds. The comparison with the commonly used CF indicates that CT has the same or higher negative correlation with the CRE of global horizontal irradiance. Moreover, the correlation between CT and CF with the CREs of direct normal irradiance (DNI) and diffuse irradiance (DFI) is explored in detail. The results indicate that CT has a more negative correlation with the CRE of DNI than that of CF when the sun is obscured, particularly for altocumulus clouds. A detailed discussion indicates that cloud radiative enhancement effects mainly occur under cumulus clouds with a minimum distance between the clouds and the sun (CSMD) of less than three solar radii, but cirrus clouds and altocumulus clouds can also induce cloud radiative enhancement effects.

SCP: Spherical-Coordinate-Based Learned Point Cloud Compression

In recent years, the task of learned point cloud compression has gained prominence. An important type of point cloud, LiDAR point cloud, is generated by spinning LiDAR on vehicles. This process results in numerous circular shapes and azimuthal angle invariance features within the point clouds. However, these two features have been largely overlooked by previous methodologies. In this paper, we introduce a model-agnostic method called Spherical-Coordinate-based learned Point cloud compression (SCP), designed to fully leverage the features of circular shapes and azimuthal angle invariance. Additionally, we propose a multi-level Octree for SCP to mitigate the reconstruction error for distant areas within the Spherical-coordinate-based Octree. SCP exhibits excellent universality, making it applicable to various learned point cloud compression techniques. Experimental results demonstrate that SCP surpasses previous state-of-the-art methods by up to 29.14% in point-to-point PSNR BD-Rate.

Designing heuristic driven hybrid optimization algorithms for efficient workflow scheduling in IaaS cloud

<p class="Abstract">Each one of the real resources or hardware like workstations, work ranges, joins, switches, switches, server ranches, and capacity contraptions are fundamental for the establishment. In disseminated computing, all the establishment is virtualized and given to infrastructure as a service. Usually called IaaS. IaaS quickly increments or down with ask and avoids the got to procure genuine servers and distinctive server cultivate establishment; each resource is displayed as a specific help portion. A conveyed computing pro-organization bargains with the establishment, whereas the client presents, plans, and manages programming, counting applications, middleware, and working systems. IaaS dispersed computing offers clients induction to figuring resources like servers, stockpiling, and frameworks organization. Affiliations utilize their possess establishment and applications interior a master organization’s system. For cloud service providers, cloud schedulers automate IT procedures. Schedulers are used by end users to automate jobs, or tasks, that support everything from big data pipelines to machine learning processes to cloud infrastructure. Infrastructure as a service, or IaaS, is a type of cloud computing that uses the internet to provide virtualized computing resources. IaaS is one of the three main types of cloud computing services, along with Platform as a Service (PaaS) and Software as a Service (SaaS). The precise task is assigned to the CPU, the network, and the storage by resource scheduling. The point behind this is the outrageous use of assets. However, both cloud providers and users require well-organized scheduling.</p>

Clouds and plant ecophysiology: missing links for understanding climate change impacts

Observations and models indicate that human activity is altering cloud patterns on a global scale. Clouds impact incident visible and infrared radiation during both day and night, driving daily and seasonal variability in plant temperatures—a fundamental driver of all physiological processes. To understand the impacts of changing cloud patterns on essential plant-based processes such as carbon sequestration and food production, changes in local cloud regimes must be linked, via ecophysiology, with affected plant systems. This review provides a comprehensive treatment of cloud effects (apart from precipitation) on fundamental ecophysiological processes that serve as the basis of plant growth and reproduction. The radiative effects of major cloud types (cumulus, stratus, cirrus) are differentiated, as well as their relative impacts on plant microclimate and physiology. Cloud regimes of major climate zones (tropical, subtropical, temperate, polar) are superimposed over recent changes in cloud cover and primary productivity. The most robust trends in changing global cloud patterns include: (i) the tropical rain belt (comprised mostly of deep convective clouds) is narrowing, shifting latitudinally, and strengthening, corresponding with shorter but more intense rainy seasons, increased clouds and precipitation in some parts of the tropics, and decreases in others; (ii) tropical cyclones are increasing in intensity and migrating poleward; (iii) subtropical dry zones are expanding, resulting in fewer clouds and drier conditions at these latitudes; (iv) summer mid-latitude storm tracks are weakening and migrating poleward, and clouds in temperate regions are decreasing; and (v) clouds over the Arctic are increasing. A reduction in coastal fog and low clouds (including those associated with montane cloud forests) have also been observed, although these trends can be partially attributed to local patterns of deforestation, urbanization, and/or reductions in aerosols associated with clean air initiatives. We conclude by highlighting gaps in the cloud-ecophysiology literature in order to encourage future research in this under-studied area.

A non‐parametric way to estimate observation errors based on ensemble innovations

AbstractPrevious studies that inferred the observation error statistics from the innovation statistics can only provide the second moment of the error probability density function (pdf). However, the observation errors are sometimes non‐Gaussian, for example, for observation operators with unknown representation errors, or for bounded observations. In this study, we propose a new method, the Deconvolution‐based Observation Error Estimation (DOEE), to infer the full observation error pdf. DOEE does not rely on linear assumptions on the observation operator, the optimality of the data assimilation algorithm, or implicit Gaussian assumptions on the error pdf. The main assumption of DOEE is the availability of an ensemble of background forecasts following the independent and identically distributed (i.i.d.) assumption. We conduct idealized experiments to demonstrate the ability of the DOEE to accurately retrieve a non‐Gaussian (bimodal, skewed, or bounded) observation error pdf. We then apply the DOEE to construct a state‐dependent observation error model for satellite radiances by stratifying the observation errors based on cloud amount. In general, we find that the observation error pdfs in many categories are skewed. By adding a new predictor, total column water vapor (TCWV), into the state‐dependent model, we find that for cloudy pixels, when TCWV is small, the observation error pdfs are quite similar and Gaussian‐like, whereas when TCWV is large, the observation error pdfs differ for different cloud amount, while all of them are positively biased. This result suggests that exploring other predictors, like cloud type, might improve the stratification of the observation error model. We also discuss ways to include a non‐parametric observation error pdf into modern data assimilation schemes, including a consideration of the strong‐constraint 4D‐Var perspective, as well as the implications for other observation types including observations with bounded range.

CUS3D: A New Comprehensive Urban-Scale Semantic-Segmentation 3D Benchmark Dataset

With the continuous advancement of the construction of smart cities, the availability of large-scale and semantically enriched datasets is essential for enhancing the machine’s ability to understand urban scenes. Mesh data have a distinct advantage over point cloud data for large-scale scenes, as they can provide inherent geometric topology information and consume less memory space. However, existing publicly available large-scale scene mesh datasets are limited in scale and semantic richness and do not cover a wide range of urban semantic information. The development of 3D semantic segmentation algorithms depends on the availability of datasets. Moreover, existing large-scale 3D datasets lack various types of official annotation data, which hinders the widespread applicability of benchmark applications and may cause label errors during data conversion. To address these issues, we present a comprehensive urban-scale semantic segmentation benchmark dataset. It is suitable for various research pursuits on semantic segmentation methodologies. This dataset contains finely annotated point cloud and mesh data types for 3D, as well as high-resolution original 2D images with detailed 2D semantic annotations. It is constructed from a 3D reconstruction of 10,840 UVA aerial images and spans a vast area of approximately 2.85 square kilometers that covers both urban and rural scenes. The dataset is composed of 152,298,756 3D points and 289,404,088 triangles. Each 3D point, triangular mesh, and the original 2D image in the dataset are carefully labeled with one of the ten semantic categories. Six typical 3D semantic segmentation methods were compared on the CUS3D dataset, with KPConv demonstrating the highest overall performance. The mIoU is 59.72%, OA is 89.42%, and mAcc is 97.88%. Furthermore, the experimental results on the impact of color information on semantic segmentation suggest that incorporating both coordinate and color features can enhance the performance of semantic segmentation. The current limitations of the CUS3D dataset, particularly in class imbalance, will be the primary target for future dataset enhancements.

ForestSemantic: a dataset for semantic learning of forest from close-range sensing

ABSTRACT Information about trees in forest is essential for the assessment of the quantity and the quality of forest ecosystem services. Recently, Deep Learning (DL) methods were regarded as a new cornerstone of algorithm development. Semantic annotations of 3D forest scenes are fundamental for DL algorithm developments. Its necessity has become more urgent as DL is data-driven and requires large amount of training and verification data. However, high-quality annotated forest datasets are still rare, as trees comprise of irregular structures and small components and pose significantly greater challenges even for manual recognition in comparison with artificial objects. This paper introduces a new open point cloud dataset ForestSemantic for forest semantic studies at both individual tree- and plot-levels. The dataset is based on TLS data with different forest conditions. Manual annotation was carried out to a level of detail 4, i.e., until all visible branches. Semantic information is provided at both plot- and tree-levels, as well as at both object- and point-level. Thus, the dataset supports both instance and semantic studies, such as objects detection and segmentation and classification at both tree- and plot-levels. In addition, the dataset also provides comprehensive structural tree traits as reference for further methodological development and verification. This dataset is expected to facilitate research in new dimensions and benchmarks of different systems and solutions. A few examples are demonstrated in this paper to unveil the potentials of the dataset for various applications. In future, it is also possible to simulate other types of point clouds by down-sampling and deforming, and to transfer the dataset for training and verification of other close-range sensing systems, as the dataset was generated using TLS point clouds that represent the highest spatial resolution and geometric accuracy in all close-range point clouds.

Implementation and evaluation of a spectral cumulus parametrization for simulating tropical cyclones in JMA‐GSM

AbstractA spectral cumulus parametrization (spectral scheme) developed using a cloud‐resolving model simulation was implemented in Global Spectral Model of the Japan Meteorological Agency (JMA‐GSM, GSM hereafter). The performance of the spectral scheme in terms of mean state, variability, and tropical cyclone (TC) properties was evaluated using atmosphere‐only model experiments by comparing results of the original convection scheme (based on Arakawa–Schubert scheme, AS scheme) and the spectral scheme. Parameter tunings were first conducted for the spectral scheme, through which the climatological errors of the cloud cover in the Southern Ocean and temperature were greatly improved. The spectral scheme showed comparable climatological errors to the AS scheme in the increased resolutions. The spectral scheme greatly improved tropical variability, that is, response to El Niño/Southern Oscillation and Madden–Julian Oscillation. Analyses on TC statistical data revealed that the spectral scheme improved TC properties in terms of genesis frequency, intensity, and the response of TC to tropical variability. Specifically, significant improvements were seen in the Northern Hemisphere. Further analyses on the TC structure revealed that higher TC intensity was sustained for longer times by the spectral scheme than by the AS scheme. The reason for this is that the spectral scheme better simulates convective heating by considering coexistence of different cloud types with parametrized vertically varying entrainment rates, especially from low to mid‐altitudes. The convective heating leads to a sharper vertical‐radial circulation in the TC structure, causing stronger incoming moisture flux toward the TC centre, and resulting in a stronger TC intensity. Consequently, the spectral scheme can simulate a comparable mean state, better variability, and better TC properties compared with the original scheme by simulating a more detailed convective structure. Therefore, it has a potential to increase the TC forecast skill for operational GSM.

Global characteristics of cloud macro-physical properties from active satellite remote sensing

Based on the products of joint CloudSat and CALIPSO observations from 2006 to 2017, this study investigates the global cloud macro-physical characteristics, including the spatial differences in cloud height, cloud thickness, cloud type, and cloud phase, along with the spatial-temporal variations and the land-sea discrepancy of the cloud vertical structure variables according to the study areas. The results show that the cloud frequency is higher over oceans than over land globally, but the opposite is true for cloud systems with more than two layers. Seasonal variability of global-average total cloud fraction is small, but large among different latitudinal bands. The global spatial distributions of cloud top height (CTH) and cloud base height (CBH) are similar, showing larger cloud heights over the land than over the ocean. The low clouds are mostly located in the central subtropical region, with CTH among 2.5–7.5 km, While the high clouds are mainly distributed in the equatorial region with CTH about 10–15 km. Meanwhile, the regional and seasonal variability of cloud geometric thickness (CGT) in all study areas are small, and the average thickness of higher clouds is slightly larger than that of lower clouds. By categorizing the clouds into eight types, it was found that the frequency of cirrus (Ci) and stratocumulus (Sc) is higher, followed by altocumulus (Ac), altostratus (As), nimbostratus (Ns) and cumulus (Cu), and the frequency of stratus (St) and deep convective clouds (DC) is the lowest. In addition, the occurrence frequency of clouds in phases was ranked from high to low as ice clouds, liquid clouds and mixed-phase clouds. Ice clouds are concentrated in the tropics and mid-latitudes, and located almost symmetrically around the equator during Mar-Apr-May (MAM) and Sep-Oct-Nov (SON). Liquid clouds are mainly distributed around 30°S and 30°N with large seasonal variability, and their frequency is higher over the ocean than over the land. Mixed-phase clouds are concentrated in the middle and high latitudes, with much less occurring in the equatorial regions.